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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coal polymer composites prepared by fused deposition modeling (FDM) 3D printing

Coal is a vital energy resource worldwide, but pollutants and greenhouse gases from its combustion cause environmental problems. To explore the non-combustion approach to use and valorize coal, anthracite and lignite were blended with polyamide 12 (PA 12) through FDM printing in this work and compared in the composites. By adding lignite, Young’s modulus improved with increasing loading to 50 wt% while tensile strength leveled off among the composites, compared to that of PA 12. By contrast, the addition of anthracite decreased the tensile performance at all loadings. Rheology tests and morphology analyses suggested that the interactions between fillers (anthracite and lignite) and PA 12 may cause differences in tensile properties. In addition, the printed lignite composites showed improved thermal conductivity (~ twofold), indicating lignite demonstrates the potential to build functional composites. This work provides a strategy to use lignite in composites by 3D printing for value-added products and reduces the demand for petroleum-based polymers. Overall, our approach diverts lignite from combustion processes and alleviates the negative impact of lignite use on the environment.

36 MATERIALS SCIENCE↗
DOE OSTI2020

In-situ X-ray scattering study of isotactic polypropylene/graphene nanocomposites under shear during fused deposition modeling 3D printing

Complementary techniques were applied to characterize the structure property relationships of FDM printed isotactic polypropylene/graphene nanocomposites. Raman spectroscopy indicated that graphene was mostly exfoliated during the nozzle extrusion process. Rheological measurements of the viscosity indicated that the presence of graphene induced shear thinning during extrusion. Azimuthal scans of wide angle x-ray scattering indicate correlated thermal fluctuations of the graphene platelets which stops abruptly at the onset of transcrystallization, or templating of the iPP chains on the graphene. X-ray microbeam SAXS analysis show the formation of ‘shish-kebab’ structures at the core of the unfilled filaments, where the ‘kebab’ component vanishes at the fiber-fiber interface enabling interdiffusion. Weak lamellar structure, and a corresponding decrease in the storage modulus are observed with the addition of 5% graphene. The lamellar structure disappears entirely with the addition of 10% graphene, while the storage modulus is decreased by nearly 50%. Thermal imaging of heat flux dissipation across molded and printed samples shows no orientational effects in pure iPP despite differences in crystal structure. Thermal conductivity increases dramatically in the samples with 10% graphene, and strong orientational effects are observed where the thermal coefficients differ by 180% between samples printed parallel or perpendicular to the heat flux.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fused Deposition Modeling Additive Manufacturing of Carbonized Structures via Waste-Enhanced Filaments

This report details the development, characterization, and use of coal-enhanced composite materials in additive manufacturing applications. High coal loading formulations—containing up to 70 wt.% coal—were successfully extruded and processed using commercially available 3D printers. Extensive experimental testing was conducted to assess the mechanical, thermal, and microstructural properties of the composites. In parallel, multi-scale computational modeling was employed to elucidate atomistic interactions and evaluate the effects of printing-induced defects on structural performance. Large-scale printability trials demonstrated the feasibility of fabricating complex components for tooling and construction applications, including wind turbine blade molds and modular wall sections. Techno-economic analyses demonstrated the cost-effectiveness and scalability of coal-enhanced composites for large-scale additive manufacturing applications such as wind turbine blade tooling.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

An Adaptive Geometry-Free Thermo-Mechanical Model for Directed Energy Deposition Process Modeling

This presentation describes a novel, geometry-free thermo-mechanical model with adaptive subdomain con- struction to accurately predict the thermal conditions, distortions, and residual stresses throughout the directed energy deposition (DED) process. A novel finite element workflow is designed to con- duct the numerical analysis, based on the multi-app and data transfer capabilities in the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). Unlike with traditional methods, the part geometry in this model is not predefined. Instead, it is a combined effect of the processing parameters and material properties. At each time step, the model utilizes a subdomain construction paradigm to model the material deposition. A specialized mesh adaptivity scheme is incorporated to provide an accurate prediction while reducing the overall computational cost. The results generated by the proposed model show general agreement with the experimental measurements for the single track scan with varying processing parameters and demonstrate reasonable predictions for higher material buildups.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling ash deposition and shedding during oxy-combustion of coal/rice husk blends at 70% inlet O2

Abstract Co-firing rice husk (RH) and coal with carbon capture using oxy-combustion presents a net carbon negative energy production opportunity. In addition, the high fusion temperature of the non-sticky, silica rich, RH can mitigate ash deposition as well as promote shedding of deposits. To identify the optimum operating conditions, fuel particle sizes, and blend ratios that minimize ash deposition, a Computational Fluid Dynamic methodology with add-on ash deposition and shedding models were employed to predict outer ash deposition and shedding rates during co-combustion of coal/RH in AIR and O 2 /CO 2 (70/30 vol%, OXY70) oxidizer compositions. After ensuring that the fly-ash particle size distributions and particle Stokes numbers near the deposition surface were accurately represented (to model impaction), appropriate models for coal ash and RH ash viscosities that were accurate in the temperature region (1200–1300 K) of interest in this study were identified. A particle viscosity and kinetic energy (PKE) based capture criterion was enforced to model the ash capture. An erosion/shedding criterion that takes the deposit melt fraction and the energy consumed during particle impact into account was also implemented. Deposition rate predictions as well as the deposition rate enhancement (OXY70/AIR) were in good agreement with measured values. While the OXY70 scenario was associated with a significant reduction (60%–70%) in flue gas velocities, it also resulted in larger fly-ash particles. As a result, the PKE distributions of the erosive RH ash were similar in both scenarios and resulted in similar shedding rates.

Energy & Fuels↗
DOE OSTI2020

What are the current limits on determination of protonation state using neutron macromolecular crystallography?

The rate of deposition of models determined by neutron diffraction, or a hybrid approach that combines X-ray and neutron diffraction, has increased in recent years. The benefit of neutron diffraction is that hydrogen atom (H) positions are detectable, allowing for the determination of protonation state and water molecule orientation. This study analyses all neutron models deposited in the Protein Data Bank to date, focusing on protonation state and properties of H (or deuterium, D) atoms as well as the details of water molecules. In particular, clashes and hydrogen bonds involving H or D atoms are investigated. As water molecules are typically the least reproducible part of a structural model, their positions in neutron models were compared to those in homologous high-resolution X-ray structures. For models determined by joint refinement against X-ray and neutron data, the water structure comparison was also carried out for models re-refined against the X-ray data alone. The homologues have generally fewer conserved water molecules where X-ray only was used and the positions of equivalent waters vary more than in the case of the hybrid X-ray model. As neutron diffraction data are generally less complete than X-ray data, the influence of neutron data completeness on nuclear density maps was also analyzed. Here, we observe and discuss systematic map quality deterioration as result of data incompleteness.

Hydrogen bond↗
DOE OSTI2022

Modeling of deposit formation in mesoporous substrates via atomic layer deposition: Insights from pore‐scale simulation

Atomic layer deposition (ALD) has been a promising technique in fabricating membranes and tuning their properties with a precision at the atomic level. Fabrication of zeolitic imidazolate framework (ZIF) membranes using the ligand-induced permselectivation (LIPS) method starts with the formation of an oxide in a mesoporous substrate by ALD and is followed by the transformation of this oxide to ZIF using imidazolate vapor treatment. The objective of the ALD step is to block the mesopores with a thin deposit, that is, one with small penetration depth and small thickness on the top surface of the substrate. Unlike typical ALD on nonporous substrates, where all available sites react per ALD cycle, thin deposit formation in a mesoporous substrate requires that only a small fraction of the available deposition sites (i.e., close to the substrate surface) is subjected to ALD. Consequently, reactant dosing and duration of pulses are important process variables which, together with diffusion and reaction kinetics determine the deposit structure. Quantitative understanding of the interplay of these variables and phenomena can enable the rational design of ALD within mesoporous substrates. Here, we extend our earlier modeling effort considering the coexistence of ALD both inside the pores and on the external surface of the substrate. Finite-volume based models were developed and validated to simulate the two distinct modes of deposition cycle by cycle. The total mass uptake of the substrate with ALD cycles can be predicted using the combined surface deposition and pore reaction–diffusion models as affirmed by in situ quartz crystal microbalance experimental data. The ALD reactor model combined with the deposition model can accurately capture the number of ALD cycles needed to block the pores of the substrate. Based on the model, we designed a modified ALD process and examined the performance of the corresponding LIPS membranes. Furthermore, the present modeling work provides a new understanding of the deposit formation via ALD within mesoporous substrates for a variety of membrane applications.

atomic layer deposition↗
DOE OSTI2023

Directed Energy Deposition Process Modeling, Validation, and Process-Informed Optimization

The directed energy deposition (DED) process, one of the most popular additive manufacturing techniques in use today, involves various complex physical mechanisms that are not yet well understood. In this regard, computational tools show promise for elucidating the manufacturing process and enabling nondestructive performance evaluations of manufactured parts. To better control and optimize the DED process?thereby improving the manufactured product? the present work develops and demonstrates a novel artificial intelligence (AI)-based process control and optimization technique. Specifically, a geometry-free thermo-mechanical model with adaptive subdomain construction is developed to accurately capture the material?s thermo-mechanical response under cyclical reheating and high cooling rates [1]. The model is demonstrated and validated with experimental measurements, in light of various geometries and processing parameters. Moreover, based on this thermo-mechanical model, a physics-informed reduced-order model is developed to enable quick predictions of the temperature field at every time step. Furthermore, an AI-based controller is developed that can adapt to the ever-changing system states by automatically adjusting the manufacturing process parameters. This entire work is based on the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) [2] and its recent integration with Libtorch (the C++ frontend of PyTorch [3]). The combined development of the adaptive material deposition scheme, thermo-mechanical model, associated reduced-order model, and AI-based process controller carries great potential for improving advanced manufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Fidelity Energy Deposition Ignition Model Coupled with Flame Propagation Models at Engine-like Flow Conditions

With the heightened pressure on car manufacturers to increase the efficiency and reduce the carbon emissions of their fleets, more challenging engine operation has become a viable option. Highly dilute, boosted, and stratified charge, among others, promise engine efficiency gains and emissions reductions. At such demanding engine conditions, the spark-ignition process is a key factor for the flame initiation propagation and the combustion event. From a computational standpoint, there exist multiple spark-ignition models that perform well under conventional conditions but are not truly predictive under strenuous engine operation modes, where the underlying physics needs to be expanded. In this paper, a hybrid Lagrangian-Eulerian spark-ignition (LESI) model is coupled with different turbulence models, grid sizes, and combustion models. The ignition model, previously developed, relies on coupling Eulerian energy deposition with a Lagrangian particle evolution of the spark channel, at every time-step. The spark channel is attached to the electrodes and allowed to elongate at a speed derived from the flow velocity. The LESI model is used to simulate spark ignition in a nonquiescent crossflow environment at engine-like conditions, using converge commercial computational fluid dynamics (CFD) solver. The results highlight the consistency, robustness, and versatility of the model in a range of engine-like setups, from typical with Reynolds-averaged Navier-Stokes (RANS) and a larger grid size to high fidelity with large-eddy simulation (LES) and a finer grid size. The flame kernel growth is then evaluated against Schlieren images from an optical constant volume ignition chamber with a focus on the performance of flame propagation models, such as G-equation and thickened flame model, versus the baseline well-stirred reactor model. Finally, future development details are discussed.

Advanced ignition modeling↗
DOE OSTI2024

Boron and lithium aqueous thermochemistry to model crud deposition in pressurized water reactors

Here, a comprehensive database of boron and lithium aqueous thermochemistry has been developed for use at elevated temperatures. The Helgeson-Kirkham-Flowers (HKF) formalism provides a framework to describe thermodynamic properties over a broad range of temperatures and pressures. Accuracy at high temperatures is vital to modeling nickel oxide and ferrite fuel deposits that occur in pressurized water nuclear reactors (PWRs). CALPHAD (CALculation of PHAse Diagrams) calculations are performed at PWR crud conditions to predict the stability regions of the solid lithium metaborate (LiBO 2 ) and lithium tetraborate (Li 2 B 4 O 7 ) precipitates. In addition, similar calculations are performed using sodium and potassium instead of lithium in order to assess the thermodynamics associated with preventing the formation of such precipitates. This database contributes to understanding of crud formation and composition and will aid in the prediction of phenomena such as Crud-Induced Power Shifts (CIPS).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Towards Spatial Control of Reaction Selectivity on Photocatalysts using Area Selective Atomic Layer Deposition on Model Dual Site Electrocatalyst Platform

Photocatalytic water splitting is a promising route to low-cost H2; however, this approach is currently limited by solar-to-hydrogen (STH) conversion efficiencies in the sub-10% range. Z-scheme water splitting, in which H2 and O2 evolving particles are operated in separate compartments and electronically coupled by a soluble redox mediator, offers improvements in STH efficiency but introduces high rates of undesired side and back reactions. Nanoscopic oxide coatings (e.g., CrOx, and SiOx) have previously been used to selectively block undesired reactants from reaching active sites; however, a coating encapsulating the entire photocatalyst particle limits activity as the particle can no longer facilitate both half reactions. In contrast to photodeposition, which may produce non-uniform overlayers due to the difficulty of controlling local electrochemical reactivity on the surface, area-selective atomic layer deposition (AS-ALD) can be used to deposit conformal ultrathin oxide coatings while preserving access to non-growth areas of a substrate surface. To develop this technique for Z-scheme photocatalysts, we performed AS-ALD of TiO2 on a dual site planar electrocatalyst based on interdigitated arrays of Pt and Au. Self-assembled monolayers of 1-octadecanethiol (ODT) were used to block growth on the Au array, resulting in a patterned surface in which only the Pt sites were encapsulated with TiO2. These electrocatalysts demonstrated localized reaction selectivity for hydrogen evolution reaction (HER) over the TiO2/Pt sites, while the uncoated Au sites retained activity towards Fe(II)/Fe(III) redox (FeRR). Under independent potential control, these microelectrodes showed that selectively deposited TiO2 coatings can suppress the rate of back reactions on neighboring active sites by an order of magnitude compared to uncoated control samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Model output from simulations of manganese-carbon interactions in temperate forest soil profiles

This archive contains model output, code, and scripts for simulations of coupled manganese-carbon cycling in temperate forest soil profiles. These model results were generated as part of a study investigating how manganese availability influences soil organic carbon stocks and demonstrating a new model framework for coupling carbon and manganese cycling. The simulations were in support of a manuscript: "Modeling interactive effects of manganese bioavailability, nitrogen deposition, and warming on soil carbon storage." The study addresses the research questions: How does Mn bioavailability, as driven by subsurface mineral properties, pH, and redox status, affect temperate forest soil organic carbon and litter carbon stocks?How is the relationship between Mn bioavailability and carbon cycling affected by changes in temperature and nitrogen deposition?"Model simulations were conducted in a reactive transport modeling framework using PFLOTRAN coupled to python. Multiple model simulations testing different Mn-bearing mineral solubilities, hydrological patterns, nitrogen deposition rates, and temperatures are included. Soil properties, including total and exchangeable Mn concentrations, are based on values reported for the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), a temperate forested watershed in central Pennsylvania, U.S.A where Mn cycling through vegetation has been documented.File formats include netCDF (.nc), python script (.py), shell script (.sh), plain text PFLOTRAN input file (.in), and plain text PFLOTRAN database file (.dat), and gzipped tar archive (tar.gz).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Warpage-Resistant, Under-Extrusion-Free, High-Surface-Quality Additive Manufacturing Process for Polyethylene-Based Composite Radiation Shielding Material

Polyethylene (PE) is one of the best shielding materials for primary space radiation due to its high hydrogen content. For effective secondary neutron shielding, boron-rich fillers are incorporated to enhance performance. The semicrystalline nature and high thermal expansion coefficient of PE impede its adoption for in situ additive manufacture in space via the fused deposition modeling (FDM) 3D printing. Here, we developed an optimized PE blend to mitigate the effects of under-extrusion and warpage. Guided by studies on extrusion and warpage, we developed an optimal set of printing parameters for the proposed PE blend. The optimum PE blend─both in its pure form and when doped with fillers─has been tested on different FDM printers. The printed structures exhibit high and uniform density, smooth surfaces, no warpage, and competitive mechanical properties. The FDM-printed plates demonstrate efficient shielding from thermal neutrons, predicted via modeling and confirmed experimentally using extended Q-range small-angle neutron scattering.

additive manufacturing↗
DOE OSTI2021

Highly Recyclable, Mechanically Isotropic and Healable 3D-Printed Elastomers via Polyurea Vitrimers

Delamination of three-dimensional (3D) printed polymer materials by fused deposition modeling (FDM) is a long-standing challenge in additive manufacturing (AM). With numerous efforts devoted to modification of commercially available thermoplastic FDM filaments, developing printing polymeric materials with new chemical design that could intrinsically improve interlayer adhesion, especially combined with other benefits, is in high demand. Herein, we developed a polyurea vitrimer with heat-driven malleability, which is printed to different 3D geometries using FDM. Significantly improved interlayer adhesion was observed by post-annealing the printed samples close to its topology-freezing transition temperature upon which fast associative dynamic covalent bonds exchange reaction occurs. Isotropic mechanical properties were achieved as demonstrated with printed tensile samples with different infill directions. Finally, the printed materials could be fully recycled for five generations with retained mechanical properties. Furthermore, the mechanical performance of the printed sample could also be repaired after damage.

36 MATERIALS SCIENCE↗